The present invention relates to a method and apparatus for inspecting solder joints, and more particularly, to a method and apparatus for inspecting solder joints on a printed circuit board (PCB) by using a circular illumination technique and a neural network classifier.
The solder joints of surface mounted components on a PCB appear in various three-dimensional (3D) shapes according to the quantity of soldering material and a soldering temperature condition, etc. Moreover, the surfaces of the solder joints are specular. To judge the soldering quality of the solder joints, there have been proposed various neural network based methods. By these known methods, the specular surfaces of the solder joints are illuminated with a lamp or lamps, light reflected from the specular surface is received in a charge coupled device (CCD) camera to acquire digitized image data of the received 3D shape information of the solder joints. The acquired 3D image data is learned and classified in a neural network by an artificial intelligence technique to recognize the shape of the solder joints, and thereby determine the soldering quality of the solder joints.
However, such a conventional neural network learning and classification method is typically based on an unsupervised version of Kohonen's learning vector quantization (LVQ) algorithm, which requires a large number of prototypes to insure satisfactory classification accuracy, thereby lowering the overall efficiency of the learning and classification process. In practice, self-organized clusters or self-organized prototypes generated in the neural network based learning process are just a measure for judging the similarity between respective test materials. As a result, the case where the inspector's intention does not correspond to the designer's.
Meanwhile, a conventional illumination method employs a plurality of lamps and to a single camera or a plurality of cameras and a single lamp, to measure the slope of the surfaces of solder joints. Here, either of the lamp(s) or the camera(s) is moved in order to acquire the image data. In such an illumination method, since various images should be recorded and analyzed every time a lamp or a camera is moved, much processing time is not only consumed but also expensive equipment is required.